Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of ...Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.展开更多
This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in ...This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.展开更多
Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constr...Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.展开更多
Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a sign...Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a significant challenge.To address this problem,this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem,a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm(ECFA),is deployed.In the proposed OPF model,the power generated from RESs is considered a dependent variable,while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs,respectively.Although the Coulomb’s and Franklin’s law algorithm(CFA)offers some advantages,it underperforms on complex optimization tasks compared to SSA,BA,SCA,ABC,and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for n...In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.展开更多
The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj...The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.展开更多
The security constrained distributed optimal power flow （DOPF） of interconnected power systems is presented. The centralized OPF problem of the multi-area power systems is decomposed into independent DOPF subproblem...The security constrained distributed optimal power flow （DOPF） of interconnected power systems is presented. The centralized OPF problem of the multi-area power systems is decomposed into independent DOPF subproblems, one for each area. The dynamic security region （DSR） to guarantee the transient stability constraints and static voltage stability region （SVSR） constraints, and line current limits are included as constraints. The solutions to the DOPF subproblems of the different areas are coordinated through a pricing mechanism until they converge to the centralized OPF solution. The nonlinear DOPF subproblem is solved by predictor-corrector interior point method （PClPM）. The IEEE three-area RTS-96 system is worked out in order to demonstrate the effectiveness of the proposed method.展开更多
This paper presents a TOPF （three-phase optimal power flow） model that represents photovoltaic systems. The PV plant is modeled in the TOPF as active and reactive power source. Reactive power can be generated or abs...This paper presents a TOPF （three-phase optimal power flow） model that represents photovoltaic systems. The PV plant is modeled in the TOPF as active and reactive power source. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. The reduction of unbalance voltage and losses in the distribution systems is obtained by actions of reactive power control of the inverter. The TOPF is formulated by current balance equations and the PV systems are modeled via an equivalent circuit. The primal-dual interior point method is used to obtain the optimal operating points for the systems for different scenarios of solar irradiance and temperature, thus providing a detailed view of the impact of photovoltaic distributed generation.展开更多
This paper addresses the problem of reducing CO<sub>2</sub> emissions by applying convex optimal power flow model to the combined economic and emission dispatch problem. The large amount of CO<sub>2&...This paper addresses the problem of reducing CO<sub>2</sub> emissions by applying convex optimal power flow model to the combined economic and emission dispatch problem. The large amount of CO<sub>2</sub> emissions in the power industry is a major source of global warming effect. An efficient and economic approach to reduce CO<sub>2</sub> emissions is to formulate the emission reduction problem as emission dispatch problem and combined with power system economic dispatch (ED). Because the traditional optimal power flow (OPF) model used by the economic dispatch is nonlinear and nonconvex, current nonlinear solvers are not able to find the global optimal solutions. In this paper, we use the convex optimal power flow model to formulate the combined economic and emission dispatch problem. The advantage of using convex power flow model is that global optimal solutions can be obtained by using mature industrial strength nonlinear solvers such as MOSEK. Numerical results of various IEEE power network test cases confirm the feasibility and advantage of convex combined economic and emission dispatch (CCEED).展开更多
Firefly algorithm is the new intelligent algorithm used for all complex engineering optimization problems. Power system has many complex optimization problems one of which is the optimal power flow (OPF). Basically, i...Firefly algorithm is the new intelligent algorithm used for all complex engineering optimization problems. Power system has many complex optimization problems one of which is the optimal power flow (OPF). Basically, it is minimizing optimization problem and subjected to many complex objective functions and constraints. Hence, firefly algorithm is used to solve OPF in this paper. The aim of the firefly is to optimize the control variables, namely generated real power, voltage magnitude and tap setting of transformers. Flexible AC Transmission system (FACTS) devices may used in the power system to improve the quality of the power supply and to reduce the cost of the generation. FACTS devices are classified into series, shunt, shunt-series and series-series connected devices. Unified power flow controller (UPFC) is shunt-series type device that posses all capabilities to control real, reactive powers, voltage and reactance of the connected line in the power system. Hence, UPFC is included in the considered IEEE 30 bus for the OPF solution.展开更多
This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimiz...This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimization(BCSBO)method,a novel modification of the original Circulatory System Based Optimization(CSBO)algorithm.The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills,making it an effective instrument for addressing intricate optimization challenges.The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples.The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches,including CSBO,Moth-Flame Optimization(MFO),Particle Swarm Optimization(PSO),Thermal Exchange Optimization(TEO),and Elephant Herding Optimization(EHO).For the first case with minimizing the fuel cost associated with the thermal power generators,the total cost reported by the BCBSO is obtained as$781.8610,which is lower than other algorithms.For the second case,aimed at minimizing the total generating cost while also imposing a fixed carbon tax for thermal units,the derived total cost by the BCBSO is$810.7654.For the third case,aimed at minimizing the total cost considering prohibited operating zones of thermal units with RESs,the obtained total cost using the BCBSO is$781.9315.For case 4,with network losses included,the value of total costs obtained using the BCBSO is$880.4864.The value of total costs considering voltage deviation in case 5 is also obtained as$961.4354.For the IEEE 118-bus test system,the total cost is obtained$103,415.9315 using the BCBSO.These values reported by the BCBSO are all lower than those obtained by other methods addressed in this paper.The findings highlight the BCSBO algorithm’s potential as a crucial tool for enhancing power systems with renewable energies.展开更多
With the rapid development of the wind generation,uncertainties of random wind and load bring some inevitable impacts on the security of power system. Once the uncertainty causes line power to exceed its limit, line o...With the rapid development of the wind generation,uncertainties of random wind and load bring some inevitable impacts on the security of power system. Once the uncertainty causes line power to exceed its limit, line overload will occur. The paper presents the risk control of transmission line overload for windintegrated power systems. Firstly, a risk control model of line overload is proposed considering the uncertainties of loads,generator outputs and wind powers. The generation cost and security level of system associated with overload can be optimally controlled. Then path following interior point method is employed to carry out the optimal control. Finally the simulation is made on the modified IEEE-30 bus system. Results show that the risk of line overload is effectively reduced through the optimization of control variables.展开更多
Power system dispatch is a general concept with a wide range of applications.It is a special category of optimization problems that determine the operation pattern of the power system,resulting in a huge influence on ...Power system dispatch is a general concept with a wide range of applications.It is a special category of optimization problems that determine the operation pattern of the power system,resulting in a huge influence on the power system security,efficiency,and economics.In this paper,the power system dispatch problem is revisited from the basis.This paper provides a categorization of the dispatch problem,especially with an emphasis on industrial applications.Then,this paper presents a detailed review of the dispatch models.The common formulations of the dispatch problem are provided.Finally,this paper discusses the solutions of the dispatch problem and lists the major challenges.展开更多
The operation complexity of the distribution system increases as a large number of distributed generators(DG)and electric vehicles were introduced,resulting in higher demands for fast online reactive power optimizatio...The operation complexity of the distribution system increases as a large number of distributed generators(DG)and electric vehicles were introduced,resulting in higher demands for fast online reactive power optimization.In a power system,the characteristic selection criteria for power quality disturbance classification are not universal.The classification effect and efficiency needs to be improved,as does the generalization potential.In order to categorize the quality in the power signal disturbance,this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances.Then,a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances.The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine,and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed.The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect,reliability,robustness,and anti-noise performance,according to the experimental results.The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF,SVM and ML-KNN schemes have 0.28,0.23 and 0.22 respectively at a noise intensity of 20 dB.The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF,SVM and ML-KNN schemes indicates 0.7,0.77 and 0.78 respectively.展开更多
Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has...Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has also increased as more devices are being connected to the network. To understand the physical laws governing the operation of the network, techniques such as optimal power flow (OPF), Economic dispatch (ED) and Security constrained optimal power flow (SCOPF) were developed. These techniques have been used extensively in network operation, planning and so on. However, an in-depth presentation showcasing the merits and demerits of these techniques is still lacking in the literature. Hence, this paper intends to fill this gap. In this paper, Economic dispatch, optimal power flow and security-constrained optimal power flow are applied to a 3-bus test system using a linear programming approach. The results of the ED, OPF and SC-OPF are compared and presented.展开更多
With the increasing integration of intermittent power sources (IPSs) into the power system, the uncertainty of IPSs requires solution and current dispatch system needs improvement. This paper aims to generate the opti...With the increasing integration of intermittent power sources (IPSs) into the power system, the uncertainty of IPSs requires solution and current dispatch system needs improvement. This paper aims to generate the optimal dispatch plan for day-ahead scheduling and real-time dispatch using the proposed model of characteristic optimal power flow (COPF). The integral time period represented by the median load point and the heavy and light load point with simplicity and accuracy. Simulation case studies on a 30-bus system </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">presented, which shows that COPF is an effective model to generate the optimal dispatch plan for power systems with high penetration of IPSs.展开更多
Nowadays electricity market industry is become a major impact on power system for privatization and deregulation of power in global wise. As per the limitation of the transmission system, the complexity arises and the...Nowadays electricity market industry is become a major impact on power system for privatization and deregulation of power in global wise. As per the limitation of the transmission system, the complexity arises and the supply fact will be with demand at the time of balancing. The congestion of the power system is occurring based on the transmission limits of the power desire and amount for operating the system. In order to avoid transmission line congestion, enhanced STF-LODF method is proposed. It shows the regulated line transmission flow with generating units by implementing renewable energy resources (RER) based on enhanced STF-LODF in power systems. It avoids the congestion of transmission line frequently in the power system and manages price based on pricing and sensitivity approach, and also manages optimal location of congestion transmission and instability issues of voltage. The congestion management of Locational Marginal Pricing (LMP) is performed with minimum line loss, less cost, line flow, better sensitivity and better performances in optimal power flow and control flow. The efficiency of the proposed power system is analyzed and verified by the simulation results of tested IEEE 14 bus system.展开更多
Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load ...Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.展开更多
<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Tr...<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimiz</span><span style="font-family:Verdana;">ing the congestion cost, this work suggests a hybrid optimization based on</span><span style="font-family:Verdana;"> two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.</span>展开更多
基金the National Natural Science Foundation of China(Grant Nos.51975048,U1764257 and 51705480)the Beijing Institute of Technology Research Fund Program for Young Scholars。
文摘Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.
文摘This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.
文摘Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.
文摘Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a significant challenge.To address this problem,this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem,a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm(ECFA),is deployed.In the proposed OPF model,the power generated from RESs is considered a dependent variable,while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs,respectively.Although the Coulomb’s and Franklin’s law algorithm(CFA)offers some advantages,it underperforms on complex optimization tasks compared to SSA,BA,SCA,ABC,and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
基金supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University in Saudi Arabia under Project Number(ICR-2024-1002).
文摘In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of China
文摘The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
基金National Natural Science Foundation of China(No.50595413)National Key Basic Research Program ("973" Program) (No.2004CB217904)
文摘The security constrained distributed optimal power flow （DOPF） of interconnected power systems is presented. The centralized OPF problem of the multi-area power systems is decomposed into independent DOPF subproblems, one for each area. The dynamic security region （DSR） to guarantee the transient stability constraints and static voltage stability region （SVSR） constraints, and line current limits are included as constraints. The solutions to the DOPF subproblems of the different areas are coordinated through a pricing mechanism until they converge to the centralized OPF solution. The nonlinear DOPF subproblem is solved by predictor-corrector interior point method （PClPM）. The IEEE three-area RTS-96 system is worked out in order to demonstrate the effectiveness of the proposed method.
文摘This paper presents a TOPF （three-phase optimal power flow） model that represents photovoltaic systems. The PV plant is modeled in the TOPF as active and reactive power source. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. The reduction of unbalance voltage and losses in the distribution systems is obtained by actions of reactive power control of the inverter. The TOPF is formulated by current balance equations and the PV systems are modeled via an equivalent circuit. The primal-dual interior point method is used to obtain the optimal operating points for the systems for different scenarios of solar irradiance and temperature, thus providing a detailed view of the impact of photovoltaic distributed generation.
文摘This paper addresses the problem of reducing CO<sub>2</sub> emissions by applying convex optimal power flow model to the combined economic and emission dispatch problem. The large amount of CO<sub>2</sub> emissions in the power industry is a major source of global warming effect. An efficient and economic approach to reduce CO<sub>2</sub> emissions is to formulate the emission reduction problem as emission dispatch problem and combined with power system economic dispatch (ED). Because the traditional optimal power flow (OPF) model used by the economic dispatch is nonlinear and nonconvex, current nonlinear solvers are not able to find the global optimal solutions. In this paper, we use the convex optimal power flow model to formulate the combined economic and emission dispatch problem. The advantage of using convex power flow model is that global optimal solutions can be obtained by using mature industrial strength nonlinear solvers such as MOSEK. Numerical results of various IEEE power network test cases confirm the feasibility and advantage of convex combined economic and emission dispatch (CCEED).
文摘Firefly algorithm is the new intelligent algorithm used for all complex engineering optimization problems. Power system has many complex optimization problems one of which is the optimal power flow (OPF). Basically, it is minimizing optimization problem and subjected to many complex objective functions and constraints. Hence, firefly algorithm is used to solve OPF in this paper. The aim of the firefly is to optimize the control variables, namely generated real power, voltage magnitude and tap setting of transformers. Flexible AC Transmission system (FACTS) devices may used in the power system to improve the quality of the power supply and to reduce the cost of the generation. FACTS devices are classified into series, shunt, shunt-series and series-series connected devices. Unified power flow controller (UPFC) is shunt-series type device that posses all capabilities to control real, reactive powers, voltage and reactance of the connected line in the power system. Hence, UPFC is included in the considered IEEE 30 bus for the OPF solution.
文摘This paper examines the intricate issue of Optimal Power Flow(OPF)optimization concerning the incorporation of renewable energy sources(RESs)into power networks.We present the Boosting Circulatory System Based Optimization(BCSBO)method,a novel modification of the original Circulatory System Based Optimization(CSBO)algorithm.The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills,making it an effective instrument for addressing intricate optimization challenges.The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples.The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches,including CSBO,Moth-Flame Optimization(MFO),Particle Swarm Optimization(PSO),Thermal Exchange Optimization(TEO),and Elephant Herding Optimization(EHO).For the first case with minimizing the fuel cost associated with the thermal power generators,the total cost reported by the BCBSO is obtained as$781.8610,which is lower than other algorithms.For the second case,aimed at minimizing the total generating cost while also imposing a fixed carbon tax for thermal units,the derived total cost by the BCBSO is$810.7654.For the third case,aimed at minimizing the total cost considering prohibited operating zones of thermal units with RESs,the obtained total cost using the BCBSO is$781.9315.For case 4,with network losses included,the value of total costs obtained using the BCBSO is$880.4864.The value of total costs considering voltage deviation in case 5 is also obtained as$961.4354.For the IEEE 118-bus test system,the total cost is obtained$103,415.9315 using the BCBSO.These values reported by the BCBSO are all lower than those obtained by other methods addressed in this paper.The findings highlight the BCSBO algorithm’s potential as a crucial tool for enhancing power systems with renewable energies.
基金National Natural Science Foundations of China(Nos.51007052,71201097)Natural Science Foundation of Shanghai,China(No.14ZR1415300)
文摘With the rapid development of the wind generation,uncertainties of random wind and load bring some inevitable impacts on the security of power system. Once the uncertainty causes line power to exceed its limit, line overload will occur. The paper presents the risk control of transmission line overload for windintegrated power systems. Firstly, a risk control model of line overload is proposed considering the uncertainties of loads,generator outputs and wind powers. The generation cost and security level of system associated with overload can be optimally controlled. Then path following interior point method is employed to carry out the optimal control. Finally the simulation is made on the modified IEEE-30 bus system. Results show that the risk of line overload is effectively reduced through the optimization of control variables.
文摘Power system dispatch is a general concept with a wide range of applications.It is a special category of optimization problems that determine the operation pattern of the power system,resulting in a huge influence on the power system security,efficiency,and economics.In this paper,the power system dispatch problem is revisited from the basis.This paper provides a categorization of the dispatch problem,especially with an emphasis on industrial applications.Then,this paper presents a detailed review of the dispatch models.The common formulations of the dispatch problem are provided.Finally,this paper discusses the solutions of the dispatch problem and lists the major challenges.
基金The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research Grant No.(DSR-2021-02-0203).
文摘The operation complexity of the distribution system increases as a large number of distributed generators(DG)and electric vehicles were introduced,resulting in higher demands for fast online reactive power optimization.In a power system,the characteristic selection criteria for power quality disturbance classification are not universal.The classification effect and efficiency needs to be improved,as does the generalization potential.In order to categorize the quality in the power signal disturbance,this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances.Then,a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances.The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine,and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed.The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect,reliability,robustness,and anti-noise performance,according to the experimental results.The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF,SVM and ML-KNN schemes have 0.28,0.23 and 0.22 respectively at a noise intensity of 20 dB.The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF,SVM and ML-KNN schemes indicates 0.7,0.77 and 0.78 respectively.
文摘Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has also increased as more devices are being connected to the network. To understand the physical laws governing the operation of the network, techniques such as optimal power flow (OPF), Economic dispatch (ED) and Security constrained optimal power flow (SCOPF) were developed. These techniques have been used extensively in network operation, planning and so on. However, an in-depth presentation showcasing the merits and demerits of these techniques is still lacking in the literature. Hence, this paper intends to fill this gap. In this paper, Economic dispatch, optimal power flow and security-constrained optimal power flow are applied to a 3-bus test system using a linear programming approach. The results of the ED, OPF and SC-OPF are compared and presented.
文摘With the increasing integration of intermittent power sources (IPSs) into the power system, the uncertainty of IPSs requires solution and current dispatch system needs improvement. This paper aims to generate the optimal dispatch plan for day-ahead scheduling and real-time dispatch using the proposed model of characteristic optimal power flow (COPF). The integral time period represented by the median load point and the heavy and light load point with simplicity and accuracy. Simulation case studies on a 30-bus system </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">presented, which shows that COPF is an effective model to generate the optimal dispatch plan for power systems with high penetration of IPSs.
文摘Nowadays electricity market industry is become a major impact on power system for privatization and deregulation of power in global wise. As per the limitation of the transmission system, the complexity arises and the supply fact will be with demand at the time of balancing. The congestion of the power system is occurring based on the transmission limits of the power desire and amount for operating the system. In order to avoid transmission line congestion, enhanced STF-LODF method is proposed. It shows the regulated line transmission flow with generating units by implementing renewable energy resources (RER) based on enhanced STF-LODF in power systems. It avoids the congestion of transmission line frequently in the power system and manages price based on pricing and sensitivity approach, and also manages optimal location of congestion transmission and instability issues of voltage. The congestion management of Locational Marginal Pricing (LMP) is performed with minimum line loss, less cost, line flow, better sensitivity and better performances in optimal power flow and control flow. The efficiency of the proposed power system is analyzed and verified by the simulation results of tested IEEE 14 bus system.
基金Technology Major Project of China Southern Power Grid Co.,Ltd.(GZ2014-2-0049).
文摘Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.
文摘<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimiz</span><span style="font-family:Verdana;">ing the congestion cost, this work suggests a hybrid optimization based on</span><span style="font-family:Verdana;"> two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.</span>
